Browser Extension LLM Context Retrieval for Recording-Based Answers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional machine learning models lack proper background and context, generate irrelevant or complex answers, and produce spurious data, leading to inefficiencies and misinformation, especially in educational settings.
Innovation Solution
A system that preprocesses prompts and selects relevant data domains from transcripts to train machine learning models, using confidence metrics to ensure accurate and context-specific answer generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning models are used to generate answers, then the system can provide quick responses, but the answers become irrelevant, too simple or too complex, and contain spurious data
Solution Approach 1:
The system performs preliminary actions by retrieving relevant context data from the recording before the user asks a question. The timestamp indicates when the question was asked, and the system proactively fetches the corresponding transcript segment in advance, so when the user asks the question, the context is already ready for immediate processing by the machine learning model, thereby maintaining both speed and accuracy.
Solution Approach 2:
The patent introduces an intermediary mechanism that acts as a bridge between the user's question and the machine learning model. This intermediary retrieves and filters relevant context data from the recording based on the timestamp, then feeds this curated context to the model. This intermediary layer ensures the model receives appropriate background information without directly processing raw, unfiltered data, thus improving answer reliability while maintaining efficiency.
2Reliability
If the machine learning model receives sufficient context data, then answer relevance improves, but data transmission time and processing complexity increase
Solution Approach 1:
The system extracts only the necessary context data from the entire recording based on the timestamp of the user's question. Instead of transmitting the complete recording or transcript, the system identifies and extracts only the specific segment relevant to the question, thereby reducing data transmission time and processing complexity while maintaining sufficient context for accurate answers.
Solution Approach 2:
The patent segments the recording into discrete time-based context windows. The timestamp from the user's question is used to identify which segment of the recording is relevant, and only that segment is retrieved and transmitted to the machine learning model. This segmentation approach allows the system to provide contextually relevant answers while minimizing data transmission time by only processing necessary portions of the recording.
3Reliability
If the system processes the entire recording for each query, then answer completeness improves, but processing efficiency decreases
Solution Approach 1:
The system extracts only the necessary context data from the entire recording based on the timestamp of the user's question. Instead of processing the complete recording for each query, the system identifies and extracts only the specific segment relevant to the question, thereby reducing processing time and resource consumption while maintaining sufficient context for accurate and complete answers.
Data Source
AI summary
Disclosed herein are methods, systems, and computer-readable media for prompting a machine learning model to generate answer data based on a recording. Some embodiments involve preprocessing a prompt corresponding to a query for a first system by receiving the prompt and a timestamp corresponding to a time position of the query in a recording, acquiring a text transcript based on the recording, and selecting, based on the timestamp and the text transcript, a first data domain from the text transcript. Some embodiments involve transmitting at least one of the prompt, the text transcript, and the first data domain to a second system, the second system including a machine learning model. Some embodiments involve generating answer data corresponding to the prompt by querying the machine learning model with the prompt, receiving answer data from the machine learning model, and transmitting the answer data to the first system.